Science Teachers' Perception of Automated Scoring Scientific Argumentation in a Classroom
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This study examines science teachers' perceptions of automated scoring systems (AS) inteaching scientific reasoning in the classroom. Using a combination of diffusion theoryand digital assessment literacy as an analytical framework, focus group interviews wereconducted to explore teachers' attitudes, understandings, and challenges with AS. Findingssuggest that teachers primarily used AS as a supplemental tool rather than a primaryassessment method, relying on its insights as an additional reference throughout the course. Teachers found that understanding the concepts underlying machine learning algorithmsnot only increased their confidence in AS, but also inspired new approaches to integrating AIwith science content. A key contribution of this study is its detailed examination of teachers'first-hand experiences with AI-enhanced learning systems, which provides insights into howAS can be effectively incorporated into science education. The findings contribute to thedesign of supportive AI-enhanced assessment environments that are aligned with teachers'instructional goals and pedagogical values.
Abstract
This study examines science teachers' perceptions of automated scoring systems (AS) inteaching scientific reasoning in the classroom. Using a combination of diffusion theoryand digital assessment literacy as an analytical framework, focus group interviews wereconducted to explore teachers' attitudes, understandings, and challenges with AS. Findingssuggest that teachers primarily used AS as a supplemental tool rather than a primaryassessment method, relying on its insights as an additional reference throughout the course. Teachers found that understanding the concepts underlying machine learning algorithmsnot only increased their confidence in AS, but also inspired new approaches to integrating AIwith science content. A key contribution of this study is its detailed examination of teachers'first-hand experiences with AI-enhanced learning systems, which provides insights into howAS can be effectively incorporated into science education. The findings contribute to thedesign of supportive AI-enhanced assessment environments that are aligned with teachers'instructional goals and pedagogical values.
참고문헌 (26)
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